Welcome to the Quiz!
This quiz contains 9 questions from a mix of 1 subtopics.
Which of the following is true about the interpretation of correlation coefficients?
a coefficient near -1 indicates no correlation
a coefficient near +1 indicates a strong negative correlation
a coefficient near 0 indicates a perfect linear correlation
a coefficient near -1 indicates a strong negative correlation
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Which of the following is true about a positive correlation?
both variables increase or decrease together
there is no clear relationship between the variables
the correlation coefficient is always close to 0
one variable increases while the other decreases
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What is the purpose of comparing the calculated correlation coefficient to a critical value at a 5% significance level?
to determine the linearity of the correlation
to determine the strength of the correlation
to determine the direction of the correlation
to determine the significance of the correlation
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What does a correlation coefficient of 1 indicate?
a perfect negative linear correlation
a strong negative correlation
a perfect positive linear correlation
no correlation
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Which of the following is true about a negative correlation?
both variables decrease together
one variable increases while the other decreases
there is no clear relationship between the variables
both variables increase together
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What does a correlation coefficient close to 0 suggest?
a strong negative correlation
a strong positive correlation
no correlation
a perfect linear correlation
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Which statistical test is used to assess the relationship between two continuous variables that are not normally distributed?
Spearman's rank correlation
chi-squared test
Pearson's linear correlation
student's t-test
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When calculating the ranks for Spearman's rank correlation, what should you do if two values are the same for one variable?
(Select all that apply)
exclude the data points
give them the same rank
give them an average rank
assign them consecutive ranks
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In Spearman's rank correlation, what is the purpose of converting the raw data values into ranks?
to compare variables measured on different scales
to eliminate the effect of outliers
to make the data easier to interpret
to normalise the data distribution
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